DEG-analysis
urwahnawaz
2024-01-12
Last updated: 2024-02-01
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Knit directory: UPF1-FMR1/
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Introduction
source(here::here("code/libraries.R"))
library(stargazer)
library(ggfortify)
library(glue)
library(cowplot)
library(broom)
library(glmpca)
library(naniar)
library(gridExtra)
library(EnsDb.Hsapiens.v86)
library(ggrepel)
library(org.Hs.eg.db)
library(msigdbr)
library(fgsea)
goSummaries <- url("https://uofabioinformaticshub.github.io/summaries2GO/data/goSummaries.RDS") %>%
readRDS() %>%
mutate(ontology = as.character(ontology))
getGeneLists <- function(pwf, goterms, genome, ids){
gene2cat <- getgo(rownames(pwf), genome, ids)
cat2gene <- split(rep(names(gene2cat), sapply(gene2cat, length)),
unlist(gene2cat, use.names = FALSE))
out <- list()
for(term in goterms){
tmp <- pwf[cat2gene[[term]],]
tmp <- rownames(tmp[tmp$DEgenes > 0, ])
out[[term]] <- tmp
}
out
}txdf = ensembldb::transcripts(EnsDb.Hsapiens.v86, return.type="DataFrame")
tx2gene = as.data.frame(txdf[,c("tx_id","gene_id", "tx_biotype")])ah <- AnnotationHub() %>%
subset(species == "Homo sapiens") %>%
subset(rdataclass == "EnsDb") %>%
subset(genome == "GRCh38")
ensDb <- ah[["AH109606"]]
grTrans <- transcripts(ensDb)
trLengths <- exonsBy(ensDb, "tx") %>%
width() %>%
vapply(sum, integer(1))
mcols(grTrans)$length <- trLengths[names(grTrans)]
genesGR = ensembldb::genes(ensDb)
transGR = transcripts(ensDb)
mcols(transGR) = mcols(transGR) %>%
cbind(
transcriptLengths(ensDb)[rownames(.), c("nexon", "tx_len")]
)
id2Name <- structure(
genesGR$gene_name,
names = genesGR$gene_id
) %>%
.[!duplicated(names(.))]salmon.files = ("/home/neuro/Documents/NMD_analysis/Analysis/Results/UPF1-FMR1/Salmon")
salmon = list.files(salmon.files, pattern = "transcripts$", full.names = TRUE)
all_files = file.path(salmon, "quant.sf")
sample_names = gsub("/home/neuro/Documents/NMD_analysis/Analysis/Results/UPF1-FMR1/Salmon/", "", salmon)
sample_names = gsub(".gz_transcripts", "", sample_names)
sample_names = gsub("\\_.*", "", sample_names)
names(all_files) <- sample_names
md = read.csv(here::here("data/Sample_info.csv"), header= TRUE) %>%
#mutate(files = file.path(salmon, "quant.sf")) %>%
dplyr::rename("names" = "GeneWiz.ID",
"Group" = "Sample.type") %>%
mutate(Group = ifelse(Group == "MC" | Group == "FC", "Control", Group)) %>%
dplyr::select(names,everything()) %>%
mutate(names = gsub("\\_.*", "", names) )
md = md[order(match(md$names, sample_names)),]
md %<>%
rownames_to_column("random") %>%
# column_to_rownames("names") %>%
dplyr::select(-random) %>%
mutate(files = all_files)
md %<>% dplyr::filter(Group != "FMR1") %>%
dplyr::filter(names != "23-LDJ6767") %>%
dplyr::filter(names != "202")
all_files = all_files[names(all_files) %in% md$names]txi_genes = tximport(all_files, type="salmon", txOut=FALSE,
countsFromAbundance="scaledTPM", tx2gene = tx2gene, ignoreTxVersion = TRUE, ignoreAfterBar = TRUE)
keep.genes = (rowSums(txi_genes$abundance >= 1 ) >= 3)Differential gene expression analysis
txi_genes_filtered = txi_genes$counts[keep.genes,]
y <- DGEList(txi_genes_filtered)
design <- model.matrix(~Batch +Group + Sex, data = md) %>%
set_colnames(gsub(pattern = "Group", replacement ="", x = colnames(.)))
y <- calcNormFactors(y)
v <- voom(y, design)
fit = lmFit(v, design) %>%
eBayes()summary(decideTests(fit, lfc =0)) (Intercept) Batch FRAX UPF1 SexM
Down 398 439 533 233 11
NotSig 2189 12093 11814 12551 13189
Up 10627 682 867 430 14UPF1 DEGs
upf1_results_lfc = topTable(fit,coef = "UPF1", number = Inf) %>%
mutate(res = ifelse(logFC > 0& adj.P.Val < 0.05, "Upregulated",
ifelse(logFC < 0 & adj.P.Val < 0.05, "Downregulated", "NotSig"))) %>%
rownames_to_column("ensembl_gene_id") %>%
mutate(SYMBOL = mapIds(org.Hs.eg.db, keys=ensembl_gene_id, column="SYMBOL",keytype="ENSEMBL", multiVals="first")) %>%
mutate(res = ifelse(logFC > 0 & adj.P.Val < 0.05, "Upregulated",
ifelse(logFC < 0 & adj.P.Val < 0.05, "Downregulated", "NotSig")))DEColours <- c("Downregulated" = "#2e294e","Upregulated" = "#720026", "NotSig" = "#E5E5E5")
volc_upf1 = upf1_results_lfc %>%
ggplot(aes(y = -log10(adj.P.Val),
x = logFC ,
colour = res,
size =-log10(adj.P.Val),
label= SYMBOL)) +
geom_point(alpha = 0.8) +
# geom_text(aes(label=ifelse(SYMBOL== "Upf1",as.character(SYMBOL),''))) +
# geom_text(aes(label= SYMBOL), subset = SYMBOL == "Upf1") +
scale_colour_manual(values = DEColours) + theme_classic() +
theme(axis.title.y = element_text(size = 12)) +
geom_hline(yintercept = -log10(0.05), color = "grey60", size = 0.5, lty = "dashed") +
labs(x = "log2 Fold Change", y = "-log10 adj p-value") +
geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") +
xlim(-8.5, 8.5) + ylim(0, 7.5)
# volc = volc_upf1+ geom_text_repel(data=subset(upf1_results_lfc , SYMBOL %in% c("UPF1", "UPF2", "UPF3B",
# "SMG5", "SMG6",
# "UPF3A", "ATF4",
# "GADD5G")),
# aes(label=SYMBOL), position=position_dodge(width = 0.9),
# vjust=-0.40, color = "black", box.padding = 0.5, fill = "white") + ggtitle("DEGs in UPF1 relative to controls using limma/voom")
#
#
# volc
my_gg = volc_upf1 + geom_point_interactive(aes(tooltip =SYMBOL, data_id = SYMBOL),
size = 1, hover_nearest = TRUE)
girafe(ggobj = my_gg)Volcano plot showing distribution of differentially expressed genes using the limma/voom pipeline
upf1_results_lfc %>%
dplyr::filter(adj.P.Val < 0.05) %>%
dplyr::select(SYMBOL,ensembl_gene_id, logFC, adj.P.Val) %>%
dplyr::arrange(adj.P.Val) %>%
DT::datatable(caption="DEGS in UPF1 relative to controls using limma/voom pipeline") - Gene ontology enrichment analysis
Gene tonic for visualisation
library(GeneTonic)
library(DESeq2)
library(AnnotationDbi)
library(pcaExplorer)
library(igraph)
library(visNetwork)
library(magrittr)
library(topGO)tbledger <- as.data.frame(upf1_results_lfc)
colnames(tbledger)[colnames(tbledger) == "P.Value"] <- "pvalue"
colnames(tbledger)[colnames(tbledger) == "logFC"] <- "log2FoldChange"
colnames(tbledger)[colnames(tbledger) == "AveExpr"] <- "baseMean"
colnames(tbledger)[colnames(tbledger) == "SYMBOL"] <- "SYMBOL"
rownames(tbledger) =tbledger$ensembl_gene_id
tbledger$baseMean <- (2^tbledger$baseMean) * mean(y$samples$lib.size) / 1e6
edger_resu <- DESeqResults(DataFrame(tbledger))
edger_resu <- DESeq2:::pvalueAdjustment(edger_resu,
independentFiltering = FALSE,
alpha = FDR, pAdjustMethod = "BH")anno_df <- data.frame(
gene_id = upf1_results_lfc$ensembl_gene_id,
gene_name = mapIds(org.Hs.eg.db, keys = upf1_results_lfc$ensembl_gene_id, column = "SYMBOL", keytype = "ENSEMBL"),
stringsAsFactors = FALSE,
row.names = upf1_results_lfc$ensembl_gene_id
)sig_upf1 =upf1_results_lfc %>%
dplyr::filter(adj.P.Val < 0.05)
topgo_all_upf1_bp <-
pcaExplorer::topGOtable(sig_upf1$SYMBOL,
upf1_results_lfc$SYMBOL,
ontology = "BP",
mapping = "org.Hs.eg.db",
geneID = "symbol",
topTablerows = 500,
do_padj = TRUE) %>%
as.data.frame() %>%
mutate(Ont = "BP") %>%
dplyr::filter(p.value_classic < 0.05)
topgo_all_upf1_bp <- shake_topGOtableResult(topgo_all_upf1_bp)
topgo_all_upf1_bp <- get_aggrscores(res_enrich = topgo_all_upf1_bp ,
res_de = edger_resu,
annotation_obj = anno_df,
aggrfun = mean)em <- enrichment_map(topgo_all_upf1_bp,
edger_resu,
n_gs = 30,
color_by = "z_score",
anno_df)
graph_vis = em %>%
visIgraph() %>%
visOptions(highlightNearest = list(enabled = TRUE,
degree = 1,
hover = TRUE),
nodesIdSelection = TRUE)
graph_vis library(plotly)
p <- enhance_table(topgo_all_upf1_bp,
edger_resu,
n_gs = 30,
annotation_obj = anno_df,
chars_limit = 60)
ggplotly(p)distilled <- distill_enrichment(topgo_all_upf1_bp,
edger_resu,
anno_df,
n_gs = Inf,
cluster_fun = "cluster_markov")
DT::datatable(distilled$res_enrich[,])- Upregulated genes
sig_upf1 =upf1_results_lfc %>%
dplyr::filter(adj.P.Val < 0.05 & logFC > 0 )
topgo_all_upf1_bp <-
pcaExplorer::topGOtable(sig_upf1$SYMBOL,
upf1_results_lfc$SYMBOL,
ontology = "BP",
mapping = "org.Hs.eg.db",
geneID = "symbol",
topTablerows = 500,
do_padj = TRUE) %>%
as.data.frame() %>%
mutate(Ont = "BP") %>%
dplyr::filter(p.value_classic < 0.05)
topgo_all_upf1_bp <- shake_topGOtableResult(topgo_all_upf1_bp)
topgo_all_upf1_bp <- get_aggrscores(res_enrich = topgo_all_upf1_bp ,
res_de = edger_resu,
annotation_obj = anno_df,
aggrfun = mean)em <- enrichment_map(topgo_all_upf1_bp,
edger_resu,
n_gs = 50,
color_by = "z_score",
anno_df)
graph_vis = em %>%
visIgraph() %>%
visOptions(highlightNearest = list(enabled = TRUE,
degree = 1,
hover = TRUE),
nodesIdSelection = TRUE)
graph_vis p <- enhance_table(topgo_all_upf1_bp,
edger_resu,
n_gs = 30,
annotation_obj = anno_df,
chars_limit = 60)
ggplotly(p)distilled <- distill_enrichment(topgo_all_upf1_bp,
edger_resu,
anno_df,
n_gs = Inf,
cluster_fun = "cluster_markov")
DT::datatable(distilled$res_enrich[,])- Downregulated genes
sig_upf1 =upf1_results_lfc %>%
dplyr::filter(adj.P.Val < 0.05 & logFC < 0 )
topgo_all_upf1_bp <-
pcaExplorer::topGOtable(sig_upf1$SYMBOL,
upf1_results_lfc$SYMBOL,
ontology = "BP",
mapping = "org.Hs.eg.db",
geneID = "symbol",
topTablerows = 500,
do_padj = TRUE) %>%
as.data.frame() %>%
mutate(Ont = "BP") %>%
dplyr::filter(p.value_classic < 0.05)
topgo_all_upf1_bp <- shake_topGOtableResult(topgo_all_upf1_bp)
topgo_all_upf1_bp <- get_aggrscores(res_enrich = topgo_all_upf1_bp ,
res_de = edger_resu,
annotation_obj = anno_df,
aggrfun = mean)em <- enrichment_map(topgo_all_upf1_bp,
edger_resu,
n_gs = 50,
color_by = "z_score",
anno_df)
graph_vis = em %>%
visIgraph() %>%
visOptions(highlightNearest = list(enabled = TRUE,
degree = 1,
hover = TRUE),
nodesIdSelection = TRUE)
graph_vis p <- enhance_table(topgo_all_upf1_bp,
edger_resu,
n_gs = 30,
annotation_obj = anno_df,
chars_limit = 60)
ggplotly(p)distilled <- distill_enrichment(topgo_all_upf1_bp,
edger_resu,
anno_df,
n_gs = Inf,
cluster_fun = "cluster_markov")
DT::datatable(distilled$res_enrich[,])FRAX DEGs
frax_results_lfc = topTable(fit,coef = "FRAX", number = Inf) %>%
mutate(res = ifelse(logFC > 0 & adj.P.Val < 0.05, "Upregulated",
ifelse(logFC < 0 & adj.P.Val < 0.05, "Downregulated", "NotSig"))) %>%
rownames_to_column("ensembl_gene_id") %>%
mutate(SYMBOL = mapIds(org.Hs.eg.db, keys=ensembl_gene_id, column="SYMBOL",keytype="ENSEMBL", multiVals="first")) %>%
mutate(res = ifelse(logFC > 0 & adj.P.Val < 0.05, "Upregulated",
ifelse(logFC < 0 & adj.P.Val < 0.05, "Downregulated", "NotSig"))) save(frax_results_lfc,upf1_results_lfc, file = here::here("output/DEG-results.Rda"))DEColours <- c("Downregulated" = "#2e294e","Upregulated" = "#720026", "NotSig" = "#E5E5E5")
volc_frax = frax_results_lfc %>%
ggplot(aes(y = -log10(adj.P.Val),
x = logFC ,
colour = res,
size =-log10(adj.P.Val),
label= SYMBOL)) +
geom_point(alpha = 0.8) +
# geom_text(aes(label=ifelse(SYMBOL== "Upf1",as.character(SYMBOL),''))) +
# geom_text(aes(label= SYMBOL), subset = SYMBOL == "Upf1") +
scale_colour_manual(values = DEColours) + theme_classic() +
theme(axis.title.y = element_text(size = 12)) +
geom_hline(yintercept = -log10(0.05), color = "grey60", size = 0.5, lty = "dashed") +
labs(x = "log2 Fold Change", y = "-log10 adj p-value") +
geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") +
xlim(-8.5, 8.5) + ylim(0, 7.5)
# volc = volc + geom_text_repel(data=subset(upf1_results_lfc , SYMBOL %in% c("UPF1", "UPF2", "UPF3B",
# "SMG5", "SMG6",
# "UPF3A", "ATF4",
# "GADD5G", "FMR1")),
# aes(label=SYMBOL), position=position_dodge(width = 0.9),
# vjust=-0.40, color = "black", box.padding = 0.5, fill = "white") + ggtitle("DEGs in FRAX relative to controls using limma/voom pipeline")
my_gg = volc_frax + geom_point_interactive(aes(tooltip =SYMBOL, data_id = SYMBOL),
size = 1, hover_nearest = TRUE)
girafe(ggobj = my_gg)Enrichment analysis
tbledger <- as.data.frame(frax_results_lfc)
colnames(tbledger)[colnames(tbledger) == "P.Value"] <- "pvalue"
colnames(tbledger)[colnames(tbledger) == "logFC"] <- "log2FoldChange"
colnames(tbledger)[colnames(tbledger) == "AveExpr"] <- "baseMean"
colnames(tbledger)[colnames(tbledger) == "SYMBOL"] <- "SYMBOL"
rownames(tbledger) =tbledger$ensembl_gene_id
tbledger$baseMean <- (2^tbledger$baseMean) * mean(y$samples$lib.size) / 1e6
edger_resu <- DESeqResults(DataFrame(tbledger))
edger_resu <- DESeq2:::pvalueAdjustment(edger_resu,
independentFiltering = FALSE,
alpha = FDR, pAdjustMethod = "BH")anno_df <- data.frame(
gene_id = frax_results_lfc$ensembl_gene_id,
gene_name = mapIds(org.Hs.eg.db, keys = frax_results_lfc$ensembl_gene_id, column = "SYMBOL", keytype = "ENSEMBL"),
stringsAsFactors = FALSE,
row.names = frax_results_lfc$ensembl_gene_id
)sig_frax=frax_results_lfc %>%
dplyr::filter(adj.P.Val < 0.05)
topgo_all_frax <-
pcaExplorer::topGOtable(sig_frax$SYMBOL,
frax_results_lfc$SYMBOL,
ontology = "BP",
mapping = "org.Hs.eg.db",
geneID = "symbol",
topTablerows = 500,
do_padj = TRUE) %>%
as.data.frame() %>%
mutate(Ont = "BP") %>%
dplyr::filter(p.value_classic < 0.05)
topgo_all_frax<- shake_topGOtableResult(topgo_all_frax)
topgo_all_frax<- get_aggrscores(res_enrich = topgo_all_frax ,
res_de = edger_resu,
annotation_obj = anno_df,
aggrfun = mean)em <- enrichment_map(topgo_all_frax,
edger_resu,
n_gs = 30,
color_by = "z_score",
anno_df)
graph_vis = em %>%
visIgraph() %>%
visOptions(highlightNearest = list(enabled = TRUE,
degree = 1,
hover = TRUE),
nodesIdSelection = TRUE)
graph_vis library(plotly)
p <- enhance_table(topgo_all_frax,
edger_resu,
n_gs = 30,
annotation_obj = anno_df,
chars_limit = 60)
ggplotly(p)distilled <- distill_enrichment(topgo_all_frax,
edger_resu,
anno_df,
n_gs = Inf,
cluster_fun = "cluster_markov")
DT::datatable(distilled$res_enrich[,])- Upregulated genes
sig_frax=frax_results_lfc %>%
dplyr::filter(adj.P.Val < 0.05 & logFC > 0 )
topgo_all_frax <-
pcaExplorer::topGOtable(sig_frax$SYMBOL,
frax_results_lfc$SYMBOL,
ontology = "BP",
mapping = "org.Hs.eg.db",
geneID = "symbol",
topTablerows = 500,
do_padj = TRUE) %>%
as.data.frame() %>%
mutate(Ont = "BP") %>%
dplyr::filter(p.value_classic < 0.05)
topgo_all_frax<- shake_topGOtableResult(topgo_all_frax)
topgo_all_frax<- get_aggrscores(res_enrich = topgo_all_frax ,
res_de = edger_resu,
annotation_obj = anno_df,
aggrfun = mean)em <- enrichment_map(topgo_all_frax,
edger_resu,
n_gs = 50,
color_by = "z_score",
anno_df)
graph_vis = em %>%
visIgraph() %>%
visOptions(highlightNearest = list(enabled = TRUE,
degree = 1,
hover = TRUE),
nodesIdSelection = TRUE)
graph_vis p <- enhance_table(topgo_all_frax,
edger_resu,
n_gs = 30,
annotation_obj = anno_df,
chars_limit = 60)
ggplotly(p)distilled <- distill_enrichment(topgo_all_frax,
edger_resu,
anno_df,
n_gs = Inf,
cluster_fun = "cluster_markov")
DT::datatable(distilled$res_enrich[,])- Downregulated genes
sig_frax=frax_results_lfc %>%
dplyr::filter(adj.P.Val < 0.05 & logFC < 0 )
topgo_all_frax <-
pcaExplorer::topGOtable(sig_frax$SYMBOL,
frax_results_lfc$SYMBOL,
ontology = "BP",
mapping = "org.Hs.eg.db",
geneID = "symbol",
topTablerows = 500,
do_padj = TRUE) %>%
as.data.frame() %>%
mutate(Ont = "BP") %>%
dplyr::filter(p.value_classic < 0.05)
topgo_all_frax<- shake_topGOtableResult(topgo_all_frax)
topgo_all_frax<- get_aggrscores(res_enrich = topgo_all_frax ,
res_de = edger_resu,
annotation_obj = anno_df,
aggrfun = mean)em <- enrichment_map(topgo_all_frax,
edger_resu,
n_gs = 50,
color_by = "z_score",
anno_df)
graph_vis = em %>%
visIgraph() %>%
visOptions(highlightNearest = list(enabled = TRUE,
degree = 1,
hover = TRUE),
nodesIdSelection = TRUE)
graph_vis p <- enhance_table(topgo_all_frax,
edger_resu,
n_gs = 30,
annotation_obj = anno_df,
chars_limit = 60)
ggplotly(p)distilled <- distill_enrichment(topgo_all_frax,
edger_resu,
anno_df,
n_gs = Inf,
cluster_fun = "cluster_markov")
DT::datatable(distilled$res_enrich[,])Overlap of UPF1 and FRAX
- Number of genes overlapping
library(VennDiagram)
x= list("UPF1 DEGs" = upf1_results_lfc$SYMBOL[upf1_results_lfc$adj.P.Val < 0.05],
"FRAX DEGs" = frax_results_lfc$SYMBOL[frax_results_lfc$adj.P.Val < 0.05])
ggvenn(x)
overlaps = calculate.overlap(x)
upf1_only = overlaps$a1[!overlaps$a1 %in% overlaps$a3]
fmr1_only = overlaps$a2[!overlaps$a2 %in% overlaps$a3]upf1_results_lfc %>%
dplyr::filter(adj.P.Val < 0.05) %>%
dplyr::select(SYMBOL,ensembl_gene_id, logFC, adj.P.Val) %>%
inner_join(frax_results_lfc %>%
dplyr::filter(adj.P.Val < 0.05) %>%
dplyr::select(ensembl_gene_id, FRAX.logFC=logFC,
FRAX.padj =adj.P.Val), by = "ensembl_gene_id") %>%
ggscatter(., x="logFC", y = "FRAX.logFC", cor.coef = TRUE, add = "reg.line",
size=5, alpha =0.6,
conf.int = TRUE, add.params = list(color = "#EF3829",
fill = "lightgray")) +
theme_bw() + ylab("FRAX (log2FoldChange)") + xlab("UPF1 (log2FoldChange)") +
geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + ggtitle("Direction of expression of the 222 overlapping genes")
Overlap of significant genes from FRAX and UPF1
- Gene ontology of overlapping genes
sig_genes =overlaps$a3
topgo_all_frax <-
pcaExplorer::topGOtable(sig_genes,
frax_results_lfc$SYMBOL,
ontology = "BP",
mapping = "org.Hs.eg.db",
geneID = "symbol",
topTablerows = 500,
do_padj = TRUE) %>%
as.data.frame() %>%
mutate(Ont = "BP") %>%
dplyr::filter(p.value_classic < 0.05)
topgo_all_frax<- shake_topGOtableResult(topgo_all_frax)
topgo_all_frax<- get_aggrscores(res_enrich = topgo_all_frax ,
res_de = edger_resu,
annotation_obj = anno_df,
aggrfun = mean)em <- enrichment_map(topgo_all_frax,
edger_resu,
n_gs = 50,
color_by = "z_score",
anno_df)
graph_vis = em %>%
visIgraph() %>%
visOptions(highlightNearest = list(enabled = TRUE,
degree = 1,
hover = TRUE),
nodesIdSelection = TRUE)
graph_vis p <- enhance_table(topgo_all_frax,
edger_resu,
n_gs = 30,
annotation_obj = anno_df,
chars_limit = 60)
ggplotly(p)distilled <- distill_enrichment(topgo_all_frax,
edger_resu,
anno_df,
n_gs = Inf,
cluster_fun = "cluster_markov")
DT::datatable(distilled$res_enrich[,])- UPF1 specific genes
upf1_results_lfc %>%
dplyr::filter(SYMBOL %in% upf1_only) %>%
dplyr::select(SYMBOL,ensembl_gene_id, logFC, adj.P.Val) %>%
inner_join(frax_results_lfc %>%
dplyr::select(ensembl_gene_id, FRAX.logFC=logFC,
FRAX.padj =adj.P.Val), by = "ensembl_gene_id") %>%
ggscatter(., x="logFC", y = "FRAX.logFC", cor.coef = TRUE, add = "reg.line",
size=5, alpha =0.6,
conf.int = TRUE, add.params = list(color = "#EF3829",
fill = "lightgray")) +
theme_bw() + ylab("FRAX (log2FoldChange)") + xlab("UPF1 (log2FoldChange)") +
geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + ggtitle("Direction of expression of the 441 UPF1 specific genes")
topgo_all_frax <-
pcaExplorer::topGOtable(upf1_only,
frax_results_lfc$SYMBOL,
ontology = "BP",
mapping = "org.Hs.eg.db",
geneID = "symbol",
topTablerows = 500,
do_padj = TRUE) %>%
as.data.frame() %>%
mutate(Ont = "BP") %>%
dplyr::filter(p.value_classic < 0.05)
topgo_all_frax<- shake_topGOtableResult(topgo_all_frax)
topgo_all_frax<- get_aggrscores(res_enrich = topgo_all_frax ,
res_de = edger_resu,
annotation_obj = anno_df,
aggrfun = mean)em <- enrichment_map(topgo_all_frax,
edger_resu,
n_gs = 50,
color_by = "z_score",
anno_df)
graph_vis = em %>%
visIgraph() %>%
visOptions(highlightNearest = list(enabled = TRUE,
degree = 1,
hover = TRUE),
nodesIdSelection = TRUE)
graph_vis p <- enhance_table(topgo_all_frax,
edger_resu,
n_gs = 30,
annotation_obj = anno_df,
chars_limit = 60)
ggplotly(p)distilled <- distill_enrichment(topgo_all_frax,
edger_resu,
anno_df,
n_gs = Inf,
cluster_fun = "cluster_markov")
DT::datatable(distilled$res_enrich[,])- FRAX specific genes
frax_results_lfc %>%
dplyr::filter(SYMBOL %in% fmr1_only) %>%
dplyr::select(SYMBOL,ensembl_gene_id, FRAX.logFC=logFC,
FRAX.padj =adj.P.Val) %>%
inner_join(upf1_results_lfc %>%
dplyr::select(ensembl_gene_id, logFC,
adj.P.Val), by = "ensembl_gene_id") %>%
ggscatter(., x="logFC", y = "FRAX.logFC", cor.coef = TRUE, add = "reg.line",
size=5, alpha =0.6,
conf.int = TRUE, add.params = list(color = "#EF3829",
fill = "lightgray")) +
theme_bw() + ylab("FRAX (log2FoldChange)") + xlab("UPF1 (log2FoldChange)") +
geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + ggtitle("Direction of expression of the 1178 FRAX specific genes")
topgo_all_frax <-
pcaExplorer::topGOtable(fmr1_only,
frax_results_lfc$SYMBOL,
ontology = "BP",
mapping = "org.Hs.eg.db",
geneID = "symbol",
topTablerows = 500,
do_padj = TRUE) %>%
as.data.frame() %>%
mutate(Ont = "BP") %>%
dplyr::filter(p.value_classic < 0.05)
topgo_all_frax<- shake_topGOtableResult(topgo_all_frax)
topgo_all_frax<- get_aggrscores(res_enrich = topgo_all_frax ,
res_de = edger_resu,
annotation_obj = anno_df,
aggrfun = mean)em <- enrichment_map(topgo_all_frax,
edger_resu,
n_gs = 50,
color_by = "z_score",
anno_df)
graph_vis = em %>%
visIgraph() %>%
visOptions(highlightNearest = list(enabled = TRUE,
degree = 1,
hover = TRUE),
nodesIdSelection = TRUE)
graph_vis p <- enhance_table(topgo_all_frax,
edger_resu,
n_gs = 30,
annotation_obj = anno_df,
chars_limit = 60)
ggplotly(p)distilled <- distill_enrichment(topgo_all_frax,
edger_resu,
anno_df,
n_gs = Inf,
cluster_fun = "cluster_markov")
DT::datatable(distilled$res_enrich[,])Enrichment analysis with other NMD LCLs
Data export
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
time zone: Australia/Adelaide
tzcode source: system (glibc)
attached base packages:
[1] grid stats4 tools stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] VennDiagram_1.7.3 futile.logger_1.4.3
[3] plotly_4.10.4 topGO_2.54.0
[5] SparseM_1.81 GO.db_3.18.0
[7] visNetwork_2.1.2 igraph_1.6.0
[9] pcaExplorer_2.28.0 GeneTonic_2.6.0
[11] fgsea_1.28.0 msigdbr_7.5.1
[13] org.Hs.eg.db_3.18.0 ggrepel_0.9.5
[15] EnsDb.Hsapiens.v86_2.99.0 gridExtra_2.3
[17] naniar_1.0.0 glmpca_0.2.0
[19] broom_1.0.5 cowplot_1.1.2
[21] glue_1.7.0 ggfortify_0.4.16
[23] stargazer_5.2.3 ngsReports_2.4.0
[25] patchwork_1.2.0 AnnotationHub_3.10.0
[27] BiocFileCache_2.10.1 dbplyr_2.4.0
[29] openxlsx_4.2.5.2 ggiraph_0.8.8
[31] DT_0.31 msigdb_1.10.0
[33] GSEABase_1.64.0 graph_1.80.0
[35] annotate_1.80.0 XML_3.99-0.16
[37] pheatmap_1.0.12 ggvenn_0.1.10
[39] MetBrewer_0.2.0 ggpubr_0.6.0
[41] venn_1.12 viridis_0.6.4
[43] viridisLite_0.4.2 tximeta_1.20.2
[45] tximport_1.30.0 goseq_1.54.0
[47] geneLenDataBase_1.38.0 BiasedUrn_2.0.11
[49] org.Mm.eg.db_3.18.0 EnsDb.Mmusculus.v79_2.99.0
[51] ensembldb_2.26.0 AnnotationFilter_1.26.0
[53] GenomicFeatures_1.54.1 AnnotationDbi_1.64.1
[55] biomaRt_2.58.0 edgeR_4.0.11
[57] limma_3.58.1 DESeq2_1.42.0
[59] SummarizedExperiment_1.32.0 Biobase_2.62.0
[61] MatrixGenerics_1.14.0 matrixStats_1.2.0
[63] GenomicRanges_1.54.1 GenomeInfoDb_1.38.5
[65] IRanges_2.36.0 S4Vectors_0.40.2
[67] BiocGenerics_0.48.1 corrplot_0.92
[69] lubridate_1.9.3 forcats_1.0.0
[71] purrr_1.0.2 readr_2.1.5
[73] tidyverse_2.0.0 stringr_1.5.1
[75] tidyr_1.3.0 scales_1.3.0
[77] data.table_1.14.10 readxl_1.4.3
[79] tibble_3.2.1 magrittr_2.0.3
[81] reshape2_1.4.4 ggplot2_3.4.4
[83] dplyr_1.1.4 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] vroom_1.6.5 progress_1.2.3
[3] Biostrings_2.70.1 vctrs_0.6.5
[5] digest_0.6.34 png_0.1-8
[7] shape_1.4.6 shinyBS_0.61.1
[9] registry_0.5-1 git2r_0.33.0
[11] MASS_7.3-60.0.1 httpuv_1.6.13
[13] foreach_1.5.2 withr_3.0.0
[15] xfun_0.41 ellipsis_0.3.2
[17] survival_3.5-7 memoise_2.0.1
[19] systemfonts_1.0.5 zoo_1.8-12
[21] GlobalOptions_0.1.2 prettyunits_1.2.0
[23] KEGGREST_1.42.0 promises_1.2.1
[25] httr_1.4.7 rstatix_0.7.2
[27] restfulr_0.0.15 ps_1.7.6
[29] rstudioapi_0.15.0 shinyAce_0.4.2
[31] miniUI_0.1.1.1 generics_0.1.3
[33] base64enc_0.1-3 processx_3.8.3
[35] babelgene_22.9 curl_5.2.0
[37] zlibbioc_1.48.0 ca_0.71.1
[39] polyclip_1.10-6 GenomeInfoDbData_1.2.11
[41] SparseArray_1.2.3 RBGL_1.78.0
[43] threejs_0.3.3 interactiveDisplayBase_1.40.0
[45] xtable_1.8-4 doParallel_1.0.17
[47] evaluate_0.23 S4Arrays_1.2.0
[49] hms_1.1.3 colorspace_2.1-0
[51] filelock_1.0.3 Rgraphviz_2.46.0
[53] shinyWidgets_0.8.1 later_1.3.2
[55] lattice_0.22-5 NMF_0.26
[57] genefilter_1.84.0 getPass_0.2-4
[59] pillar_1.9.0 nlme_3.1-164
[61] iterators_1.0.14 gridBase_0.4-7
[63] compiler_4.3.2 stringi_1.8.3
[65] shinycssloaders_1.0.0 Category_2.68.0
[67] TSP_1.2-4 dendextend_1.17.1
[69] GenomicAlignments_1.38.2 plyr_1.8.9
[71] crayon_1.5.2 abind_1.4-5
[73] BiocIO_1.12.0 ggdendro_0.1.23
[75] locfit_1.5-9.8 bit_4.0.5
[77] fastmatch_1.1-4 whisker_0.4.1
[79] codetools_0.2-19 crosstalk_1.2.1
[81] bslib_0.6.1 GetoptLong_1.0.5
[83] mime_0.12 splines_4.3.2
[85] circlize_0.4.15 Rcpp_1.0.12
[87] tippy_0.1.0 cellranger_1.1.0
[89] knitr_1.45 blob_1.2.4
[91] utf8_1.2.4 here_1.0.1
[93] clue_0.3-65 BiocVersion_3.18.1
[95] fs_1.6.3 backbone_2.1.2
[97] admisc_0.34 expm_0.999-9
[99] ggsignif_0.6.4 Matrix_1.6-5
[101] callr_3.7.3 statmod_1.5.0
[103] tzdb_0.4.0 visdat_0.6.0
[105] tweenr_2.0.2 pkgconfig_2.0.3
[107] cachem_1.0.8 RSQLite_2.3.5
[109] DBI_1.2.1 fastmap_1.1.1
[111] rmarkdown_2.25 shinydashboard_0.7.2
[113] Rsamtools_2.18.0 sass_0.4.8
[115] BiocManager_1.30.22 carData_3.0-5
[117] farver_2.1.1 mgcv_1.9-1
[119] AnnotationForge_1.44.0 yaml_2.3.8
[121] rtracklayer_1.62.0 cli_3.6.2
[123] webshot_0.5.5 lifecycle_1.0.4
[125] lambda.r_1.2.4 backports_1.4.1
[127] rintrojs_0.3.4 BiocParallel_1.36.0
[129] timechange_0.3.0 gtable_0.3.4
[131] rjson_0.2.21 ggridges_0.5.5
[133] parallel_4.3.2 jsonlite_1.8.8
[135] colourpicker_1.3.0 seriation_1.5.4
[137] bitops_1.0-7 assertthat_0.2.1
[139] bit64_4.0.5 zip_2.3.0
[141] heatmaply_1.5.0 bs4Dash_2.3.0
[143] futile.options_1.0.1 highr_0.10
[145] jquerylib_0.1.4 lazyeval_0.2.2
[147] pander_0.6.5 shiny_1.8.0
[149] dynamicTreeCut_1.63-1 htmltools_0.5.7
[151] formatR_1.14 rappdirs_0.3.3
[153] XVector_0.42.0 RCurl_1.98-1.14
[155] rprojroot_2.0.4 ComplexUpset_1.3.3
[157] R6_2.5.1 labeling_0.4.3
[159] cluster_2.1.6 rngtools_1.5.2
[161] DelayedArray_0.28.0 tidyselect_1.2.0
[163] ProtGenerics_1.34.0 GOstats_2.68.0
[165] ggforce_0.4.1 xml2_1.3.6
[167] car_3.1-2 munsell_0.5.0
[169] htmlwidgets_1.6.4 ComplexHeatmap_2.18.0
[171] RColorBrewer_1.1-3 rlang_1.1.3
[173] uuid_1.2-0 fansi_1.0.6